How to use cloud platforms for your data science projects
As data scientists deal with solving complex business problems through building models and deploying algorithms, the right kind of tools become essential to effectively manage different aspects of a project pipeline. Taking your data science project to the cloud comes with advantages like the ability to scale, access to all the latest tools, and less maintenance from the user side. Some of the most common cloud-based platforms for data science projects include Amazon Web Services, Google Cloud Platform, IBM Watson and Microsoft Azure.
IBM provides the tools for machine learning and automation to support the entire data science lifecycle, right from preparing and exploring the data to deploying and monitoring the models.
It allows data scientists to build, run and manage AI models anywhere on IBM Cloud Pak for Data. It brings open-source frameworks like PyTorch, TensorFlow and scikit-learn along and its entire ecosystem of tools for code-based and visual data science. It works with JupyterLab and CLIs and is compatible with languages such as Python, R and Scala.
This helps collect, explore and analyse the data across any cloud with a fully integrated data and AI platform. IBM says that IBM Cloud Pak delivers a data fabric to connect and access siloed data on-premises (or across multiple clouds) without moving it. It also accelerates insights with an integrated modern cloud data warehouse.
It is a visual data science and machine learning solution that helps enterprises by accelerating time for operational tasks for data scientists. It is mainly used for data preparation and discovery, predictive analytics, model management and deployment. It also comes with IBM Cloud Pak for Data which lets one run the SPSS Modeler on the public cloud.
One of the best names when it comes to cloud-based platforms, Google Cloud is a top choice for data scientists.
Here, one can build data ingestion and preprocessing pipelines with Dataflow, a managed Apache Beam service. For a scalable messaging system to help ingest data, one can consider Cloud Pub/Sub, a global and horizontally scalable messaging infrastructure. To automate data movement to BigQuery, one can use BigQuery Data Transfer Service. For transferring data to Cloud Storage, Storage Transfer Service can be an option.
Data exploration includes slicing and dicing data through data preprocessing. Google Cloud provides many ways to explore, preprocess, and uncover insights in the data. For a notebook-based end-to-end data science environment, Vertex AI Workbench is a good option that allows accessing, analysing, and visualising the entire data.
